Volume 16, Issue 1 (Spring (COVID-19 and Older Adults) 2021)                   Salmand: Iranian Journal of Ageing 2021, 16(1): 102-111 | Back to browse issues page

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Dadgari A, Mirrezaei S M, Talebi S S, Alaghemand Gheshlaghi Y, Rohani-Rasaf M. Investigating Some Risk Factors Related to the COVID-19 Pandemic in the Middle-aged and Elderly. Salmand: Iranian Journal of Ageing 2021; 16 (1) :102-111
URL: http://salmandj.uswr.ac.ir/article-1-2189-en.html
1- Department of Nursing, School of Nursing and Midwifery, Shahroud University of Medical Sciences, Shahroud, Iran.
2- Behavioral and Social Sciences Research Center in Health, Shahroud University of Medical Sciences, Shahroud, Iran.
3- Department of Epidemiology, School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran.
4- Student Research Committee, School of Nursing and Midwifery, Shahroud University of Medical Sciences, Shahroud, Iran.
5- Department of Epidemiology, School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran. , rohani@shmu.ac.ir
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Extended Abstract

1. Introduction

The Coronavirus Disease 2019 (COVID-19) pandemic is a global emergency [1]. Although the disease has been observed in all age groups, the virus is more threatening in the elderly, and the risk of death increases with age [2]. Especially, the elderly are bio-socially at higher risks than other age groups [3, 4]. Numerous symptoms in the elderly are atypical. Furthermore, poor immune responses and underlying diseases put the elderly at a much higher risk for coronavirus infection and death, compared to other age groups [5]. Epidemiological research emphasizes our insufficient knowledge about Coronavirus Disease 2019 (COVID-19) [6]; there remain several questions about the relationship between different variables in the incidence of this disease. This study aimed to identify the association between some risk factors and Coronavirus Disease 2019 (COVID-19) in middle age and old age groups.

2. Methods and Materials

This cross-sectional study used the data of continuous registration of suspected COVID-19 cases in Shahroud City, Iran (plan 98126). In this comprehensive study, the form related to information, baseline, and having diseases for all suspected individuals referring to the registration centers in Shahroud University of Medical Sciences from February 20 to June 20, 2020, was registered. In this study, the basis for the initial diagnosis of COVID-19 in clients was a positive Reverse Transcription Polymerase Chain Reaction (RT-PCR) test based on nasal or nasopharyngeal swap or Computed Tomography (CT) scan. The inclusion criteria of this study were the referral of individuals aged over 50 years who were suspected of having the disease to the COVID-19 registration centers at the university. The basis for determining the age of 50 years as a criterion for entry was the possible study of problems from middle age [7]. The data used included demographic information, such as age, gender, educational level, occupational status, Body Mass Index (BMI), as well as history of smoking and underlying diseases. The method of measuring all demographic variables and the history of the disease was self-reported. To observe all ethical issues, the research license was obtained from the Vice Chancellor for Research of the University (Code:1399.052 IR.SHMU.REC). All research participants provided a signed informed consent form. The study subjects were assured that their provided information remains confidential.
Data analysis was performed using SPSS. Percentage and frequency were used concerning descriptive statistics. In the inferential statistics section, the Independent Samples t-test for quantitative variables and Chi-squared test for qualitative variables, as well as univariate and multivariate logistic regression models were used. The collected data were also managed to identify outdated, irrelevant, and missing data.

3. Results

Out of 3945 patients suspected of having COVID-19, after eliminating the missing cases, 1348 individuals were over 50 years of age. Of these, 602 were diagnosed with COVID-19. The Mean±SD age of the study participants was 66.62±11.33 years. The highest majority of the examined patients were hospitalized (83.8%) and retired or housewives (80.6%). The highest proportion of patients were males (50.3%) and in the age group of 60-69 years (34.1%).
 Based on the obtained results, a significant relationship was observed between BMI (P=0.001) and COVID-19. The reports of the study of clients suspected of COVID-19 in terms of disease history suggested that among the patients aged over 50 years, 801(59.4%) had cardiovascular disease, 348(25.8%) diabetes, 121(9%) chronic kidney disease, 90 (6.7%) asthma, 45(3.3%) cancer, 33(2.4%) chronic liver disease, and 20(1.5%) had acute respiratory disease. The highest prevalence of underlying diseases in definitive patients with COVID-19 was related to heart disease (41.2%), diabetes (30.1%), chronic kidney disease (9.5%), and asthma (8.6%). Among these patients, a significant association was observed between the underlying diseases of asthma and diabetes and COVID-19.
Due to the small frequency of some underlying diseases, such as asthma, acute respiratory disease, liver conditions, kidney diseases, and cancer, these patients were combined and entered into the model as a variable of underlying disease. Table 1 presents the relationship between some variables and COVID-19 by the logistic regression model. According to the univariate logistic regression findings, BMI, diabetes mellitus, and underlying diseases were associated with COVID-19; increased BMI unit by 4% and diabetes by 41% increased the risk of COVID-19. All variables were entered into a multivariate regression model by the Entering method to calculate their simultaneous effects. After entering the variables into multivariate logistic regression and observing the effects of individual variables in the presence of other variables, almost similar results were obtained for univariate regression. In this model, increasing each unit of BMI by 4% and diabetes by 38% increased the odds of developing COVID-19. Furthermore, having any underlying diseases, including asthma, acute respiratory disease, liver, and kidney conditions, and cancer borderline increased the risk of COVID-19 by 32%.

4. Discussion and Conclusion

This study signified significant findings by emphasizing the identification of risk factors for COVID-19 in the middle age and old age populations. One of the essential findings in this study was to identify a significant relationship between BMI and COVID-19 in the elderly (P=0.002). In other words, for each unit increase in BMI, there was a 4% increase in the risk of developing COVID-19 disease. In addition, the collected results indicated an association between having diabetes and generating COVID-19 (P=0.014); accordingly, a history of diabetes increased the risk of COVID-19 by 41% and the disease in the presence of other variables also increased the risk by 38%. The collected results also revealed that high BMI and diabetes in middle-aged and elderly participants are important risk factors for developing COVID-19.

Ethical Considerations

Compliance with ethical guidelines

This study was approved by the Ethics Committee of the Shahroud University of Medical Sciences (Code: IR.SHMU.REC 1399.052). All ethical principles are considered in this article. The participants were informed about the purpose of the research and its implementation stages. They were also assured about the confidentiality of their information and were free to leave the study whenever they wished, and if desired, the research results would be available to them.


This study was supported by Shahroud University of Medical Sciences (Project Code: 98126). 

Authors' contributions

Conceptualization: Marzieh Rouhani Rasaf, Ali Dadgari, Seyed Mohammad Mirrezaei and Yasman Alaghemand Gheshlaghi; Research: Marzieh Rouhani Rasaf, Ali Dadgari, Seyedeh Solmaz Talebi, Seyed Mohammad Mirrezaei and Yasman Alaghemand Gheshlaghi; Editing and finalization written: Ali Dadgari, Seyed Mohammad Mirrezaei and Seyedeh Solmaz Talebi, Marzieh Rouhani Rasaf.

Conflicts of interest

The authors declared no conflict of interest.


We would like to thank Dr. Mohammad Hassan Emamian for his kind. contribution to this paper. 


  1. Gallo Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, et al. Predictors of COVID-19 severity: A literature review. Reviews in Medical Virology. 2021; 31(1):1-10. [DOI:10.1002/rmv.2146] [PMID] [PMCID]
  2. Muselli M, Cofini V, Desideri G, Necozione S. Coronavirus (Covid-19) pandemic: How may communication strategies influence our behaviours? International Journal of Disaster Risk Reduction. 2021; 53:101982. [DOI:10.1016/j.ijdrr.2020.101982] [PMID] [PMCID]
  3. Eurosurveillance Editorial Team. Note from the editors. World Health Organization declares novel coronavirus (2019-nCoV) sixth public health emergency of international concern. Euro Surveillance: Bulletin Europeen Sur Les Maladies Transmissibles. 2020; 25(5):200131e. [DOI:10.2807/1560-7917.ES.2020.25.5.200131e]
  4. Robinson E, Boyland E, Chisholm A, Harrold J, Maloney NG, Marty L, et al. Obesity, eating behavior and physical activity during COVID-19 lockdown: A study of UK adults. Appetite. 2021; 156:104853. [DOI:10.1016/j.appet.2020.104853] [PMID] [PMCID]
  5. Lake MA. What we know so far: COVID-19 current clinical knowledge and research. Clinical Medicine. 2020; 20(2):124-7. [DOI:10.7861/clinmed.2019-coron] [PMID] [PMCID]
  6. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 2020; 395(10223):497-506. [DOI:10.1016/S0140-6736(20)30183-5]
  7. Mirhosseini S, Dadgari A, Basirinezhad MH, Mohammadpourhodki R, Ebrahimi H. The role of hope to alleviate anxiety in COVID-19 outbreak among community dwellers: An online cross-sectional survey. Annals of the Academy of Medicine, Singapore. 2020; 49(10):723-30. [DOI:10.47102/annals-acadmedsg.2020341]
  8. Liu L, GaoJY, Hu WM, Zhang XY, Guo L, Liu C-Q, et al. Clinical characteristics of 51 patients discharged from hospital with COVID-19 in Chongqing China. MedRxvi. 2020. [DOI:10.1101/2020.02.20.20025536]
  9. Azarnivand M, Alizadeh F, Soltani Z, Hojati H, Dadgari A, Emamian MH. Low social support among the elderly. Iranian journal of Public Health. 2019; 48(9):1756-7. [DOI:10.18502/ijph.v48i9.3043]
  10. Dadgari A, Hojati H, Mirrezaie SM. The relationship between the risk of falling and fear of falling among aged hospitalized patients. Nursing Practice Today. 2020; 7(1):30-7. [DOI:10.18502/npt.v7i1.2297]
  11. Woolf SH, Chapman DA, Lee JH. COVID-19 as the Leading Cause of Death in the United States. JAMA. 2021; 325(2):123-4. [DOI:10.1001/jama.2020.24865]
  12. Andrew MK, McElhaney JE. Age and frailty in COVID-19 vaccine development. Lancet. 2021; 396(10267):1942-4. [DOI:10.1016/S0140-6736(20)32481-8]
  13. Gu T, Mack JA, Salvatore M, Prabhu Sankar S, Valley TS, Singh K, et al. Characteristics associated with racial/ethnic disparities in COVID-19 outcomes in an academic health care system. JAMA Netw Open. 2020; 3(10):e2025197. [DOI:10.1001/jamanetworkopen.2020.25197] [PMID] [PMCID]
  14. Michalakis K, Panagiotou G, Ilias I, Pazaitou-Panayiotou K. Obesity and COVID-19: A jigsaw puzzle with still missing pieces. Clinical Obesity. 2021; 11(1):e12420. [DOI:10.1111/cob.12420] [PMID] [PMCID]
  15. Tracey KJ. Physiology and immunology of the cholinergic antiinflammatory pathway. The Journal of Clinical investigation. 2007; 117(2):289-96. [DOI:10.1172/JCI30555] [PMID] [PMCID]
  16. Batsis JA, Daniel K, Eckstrom E, Goldlist K, Kusz H, Lane D, et al. Promoting Healthy Aging During COVID-19. Journal of the American Geriatrics Society. 2021; 69(3):572-80. [doi: 10.1111/jgs.17035]
  17. Emamian MH, Chaman R, Khosravi A, Sheibani H, Binesh E, Vahedi H, et al. Integration of Research, Public Health, and Hospital Interventions as a Successful Model for Controlling COVID-19 Pandemic: A Perspective. Authorea. 2020. [DOI:10.22541/au.159223916.69766417]
  18. Khosravi A, Chaman R, Rohani-Rasaf M, Zare F, Mehravaran S, Emamian MH. The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran. Epidemiology and Infection. 2020; 148:e115. [DOI:10.1017/S0950268820001247] [PMID] [PMCID]
  19. Yang J, Hu J, Zhu C. Obesity aggravates COVID-19: A systematic review and meta-analysis. Journal of Medical Virology. 2021; 93(1):257-61. [DOI:10.1002/jmv.26237] [PMID] [PMCID]
  20. Lighter J, Phillips M, Hochman S, Sterling S, Johnson D, Francois F, et al. Obesity in patients younger than 60 years is a risk factor for COVID-19 hospital admission. Clinical Infectious Diseases. 2020; 71(15):896-7. [DOI:10.1093/cid/ciaa415] [PMID] [PMCID]
  21. Kassir R. Risk of COVID-19 for patients with obesity. Obesity reviews. 2020; 21(6):e13034. [DOI:10.1111/obr.13034] [PMID] [PMCID]
  22. Sadeghi A, Maleki Jamasbi M, Azami H, Karami M, Marziyeh Hassanian Z. [Survey of the relationship between demographic characteristics and organizational citizenship behavior in nurses in hamadan educational centers (Persian)]. Avicenna Journal of Nursing and Midwifery Care. 2019; 27(5):326-34. [DOI:10.30699/ajnmc.27.5.326]
  23. Holman N, Knighton P, Kar P, O’Keefe J, Curley M, Weaver A, et al. Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: A population-based cohort study. The Lancet Diabetes & Endocrinology. 2020; 8(10):823-33. [DOI:10.1016/S2213-8587(20)30271-0]
  24. Marhl M, Grubelnik V, Magdič M, Markovič RJD. Diabetes and metabolic syndrome as risk factors for COVID-19. Diabetes & Metabolic Syndrome. 2020; 14(4):671-7. [DOI:10.1016/j.dsx.2020.05.013] [PMID] [PMCID]
  25. Gralinski LE, MenacheryVD. Return of the Coronavirus: 2019-nCoV. Viruses. 2020; 12(2):135. [DOI:10.3390/v12020135] [PMID] [PMCID]

Type of Study: Research | Subject: Clinical
Received: 2021/02/23 | Accepted: 2021/03/15 | Published: 2021/04/01

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